基于Kriging代理模型的优化方法是一种有望大大提高航空航天领域复杂设计问题效率的新方法，被称为区别于传统的梯度优化与非梯度优化的第三类优化算法。但是，该方法目前适用于直到10维左右的优化问题;对于基于高可信度CFD的高维气动优化设计问题(100维左右)，该方法面临CFD分析次数过多、计算量过大的困难。高维问题成为限制其进一步应用的"瓶颈"。本项目基于一种改进的co-Kriging模型，通过在优化过程中同时引入adjoint梯度求解和低可信度CFD分析，从而提出一种适用于高维优化问题的全局优化框架，并发展出相应的统计学寻优机制和约束处理方法。通过发展基于co-Kriging模型的新的优化理论和算法，有望解决代理模型优化方法在高维优化问题方面面临的困难，为飞行器气动优化设计及其它学科相关研究提供一种比传统的代理模型优化更有效的方法。

Surrogate-based optimization via Kriging model is a new methodology which can be used to greatly improve the design efficiency of complex optimization problems in aerospace applications. It has been classified as the third type of optimization algorithm, other than the conventional gradient-based and gradient-free numerical optimization algorithms. However, this method, up to now, is typically limited to the applications with the number of design variables being about 10. For the higher -dimensional aerodynamic design optimization problems (typically about 100) based on high-fidelity computation fluid dynamics (CFD), this method is suffering from the limitations of requiring extremly large number of CFD analysis, thus prohibitive computational cost. The "size of the problem", or the so-called "curse of dimensionality" has become a "bottleneck" before it is further extended to the real-world applications. This project aims to develop the new concept of a variable-fidelity, global optimization framework which is supposed to be able to breakthrough the "bottleneck" of the surrogate-based optimization for higher-dimensional problems. The basic idea is to use an improved co-Kriging surrogate modeling which simultaneously incorporates gradient solution by Adjoint method and the solution by lower-fidelity CFD analysis. This study will address the key issues such as how to efficiently build an accurate co-Kriging model, and how to quickly find the global optimum as well as how to handle with the multiple constraints based the statistical uncertainty estimation of a co-Kriging model. Through developing such an innovative optimzation method based on co-Kriging model, we expect to work out a more efficient and practical optimization algorithm,which can stimulate the scientific research as well as the enginering design applications in this area. The resulting algorithm can be potentially applied to the aerodynamic design of an aircraft or other disciplines such as structural design where numerial analysis methods of varying fidelity are in use.